In many industries, large numbers of providers and suppliers of services and goods are employed. For example, in the health care industry, a large number of providers of medical services and suppliers of medical and related goods are involved in the delivery of health care to a given population. Certain entities, such as insurors, government payors and others must often process and pay large numbers of claims submitted by these providers and suppliers in relatively short time periods. The existence of, and the potential for, abuse of existing administrative systems by fraudulent providers or suppliers is a problem which exists in the health care setting, and in other analogous settings as well.
An objective of the present invention is to provide an automated system for processing a large number of claims submitted to a payor to identify patterns in the claim data which may be indicative of a fraudulent provider or supplier. Another objective of the invention is to provide such a system which is capable of processing claim data and identifying a potentially fraudulent provider or supplier before payments of suspect claims are made.
A preferred embodiment of the invention utilizes two popular classes of artificial intelligence: neural networks and expert systems. A neural network is a computer program which attempts to model, albeit crudely, the workings of the human brain. Neural networks are trained by example and generally excel at tasks involving pattern recognition. Expert systems are rules-based systems that deduct a solution or answer to a problem based on a series of "if . . . then" statements. An expert system attempts to mimic the deductive reasoning that an expert would employ in solving a given problem.